Sequential assimilation of multi-mission dynamical topography into a global finite-element ocean model
1Alfred Wegener Institute for Polar and Marine Research, Bussestrasse 24, 27570 Bremerhaven, Germany
2Deutsches Geodätisches Forschungsinstitut, Alfons-Goppel-Strasse 11, 80539 Munich, Germany
*now at: Département des Sciences de la Terre et de l'Atmosphère, Université du Québec à Montréal, C. P. 8888 Succ. Centre-ville Montréal, Québec, H3C 3P8 Canada
Abstract. This study focuses on an accurate estimation of ocean circulation via assimilation of satellite measurements of ocean dynamical topography into the global finite-element ocean model (FEOM). The dynamical topography data are derived from a complex analysis of multi-mission altimetry data combined with a referenced earth geoid. The assimilation is split into two parts. First, the mean dynamic topography is adjusted. To this end an adiabatic pressure correction method is used which reduces model divergence from the real evolution. Second, a sequential assimilation technique is applied to improve the representation of thermodynamical processes by assimilating the time varying dynamic topography. A method is used according to which the temperature and salinity are updated following the vertical structure of the first baroclinic mode. It is shown that the method leads to a partially successful assimilation approach reducing the rms difference between the model and data from 16 cm to 2 cm. This improvement of the mean state is accompanied by significant improvement of temporal variability in our analysis. However, it remains suboptimal, showing a tendency in the forecast phase of returning toward a free run without data assimilation. Both the mean difference and standard deviation of the difference between the forecast and observation data are reduced as the result of assimilation.
Skachko, S., Danilov, S., Janjić, T., Schröter, J., Sidorenko, D., Savcenko, R., and Bosch, W.: Sequential assimilation of multi-mission dynamical topography into a global finite-element ocean model, Ocean Sci., 4, 307-318, doi:10.5194/os-4-307-2008, 2008.